In a tabular database made up of rows and columns, item values of the same item are stored in cells of the same column, and item values of respective items associated with each other are stored in cells of the same row. Item values of respective columns associated with each other constitute one row. There may be one or more cells in which no item value is stored.
It is possible to designate a column in the table, and select a row whose cell in the designated column stores a value designated beforehand or a value in a range designated beforehand. The designated column is called a search item, and the designated item value or item value range is called a search key.
The tabular database is searched by designating the search column and the search key and obtaining the row having the designated search key in the designated column.
In other words, rows in the tabular database are classified according to the search key. That is, the search key set beforehand enables classification. The search key is simply data expressed in letters.
There is a logic operation method called symbolic logic. For example, items which are logic variables denoted by A and B are related to each other in a form such as “A OR B” or “A AND B”. Here, the relatedness between items is uniformly of the same strength. A method of performing logic operations, search, or inference using such symbolic logic is available. Not only a simple search condition using a single search key but also a more complex search condition can be generated by connecting a plurality of search keys by one or more logic operators such as AND, OR, NOT, and so on. In this method, association between items in a logic expression is fixed.
There is an inference technique called a Bayesian network. An event is regarded as an abstract entity. An abstract entity A and an abstract entity B are associated with each other, and a numeric value is added to the association. The numeric value is a probability of “If B then A”. When B is associated with A and also an abstract entity C which is an event is associated with A, combining the probability of A and B and a probability of A and C can yield a probability of “If B and C then A”. Only in the case where there is a dependency relationship between B and C, the probability of B and the probability of C can be combined as mentioned above. Inference is performed by selecting an event associated at a higher probability.
There is a method of recognizing image data or the like by imitating connections between neurons called a neural network. In this method, one piece of input data is segmented into a plurality of pieces of data. For example, one piece of image data is partitioned like a grid. Each cell of the grid of the image data is assigned a numeric value representing brightness of the cell. Pattern data is generated by arranging these numeric value data in predetermined order. A weight is set for a position of each cell beforehand. A product of the numeric value data representing the brightness of each cell and the weight of the position corresponding to the numeric value data is calculated, and the calculated products are summed. Whether or not the sum exceeds a predetermined value is determined. In the case where the sum exceeds the predetermined value, a value representing “true” is outputted from the neural network. In this method, one piece of input data as a whole is not one logic variable, but each piece of data obtained by segmenting one piece of input data is a logic variable. A product of each input data segment and its corresponding weight is calculated, and the calculated products are summed. Thus, one piece of input data as a whole is not treated as one logic variable in a logic operation.